This section is intended to provide a background or context to this invention. The description herein may include concepts that could be pursued, but are not necessarily ones that have been previously conceived or pursued. Therefore, unless otherwise indicated herein, what is described in this section is not prior art to the description and claims in this application and is not admitted to be prior art by inclusion in this section.
The following abbreviations are utilized herein:                AIC Akaike Information Criterion        BER bit error rate        BS base station        CDF cumulative density function        FFT fast Fourier transform        i.i.d. independent identically distributed        IRC interference rejection combining        ITU International Telecommunications Union        MDL minimum description length        MIMO multi-input multiple-output        MRC maximum ratio combining        NBI narrowband interference        QPSK quadrature phase shift keying        OFDM orthogonal frequency division multiplexing        PDF probability density function        QOS quality of service        SIR signal to interference ratio        SINR signal to interference plus noise ratio        SNR signal to noise ratio        SVD singular value decomposition        UWB ultra wide band        
Multiple-input multiple-output (MIMO) has the potential for achieving a high data rate and providing more reliable reception performance. Orthogonal frequency division multiplexing (OFDM) can be used to make wideband frequency-selective channels to be a number of parallel narrowband sub-channels by splitting one data stream into several parallel streams. As a result, the combination of MIMO and OFDM can be used to provide many options in space, time and frequency domains. MIMO-OFDM systems are promising candidates for several wireless systems, such as 3GPP LTE, 802.16, 4G wireless systems, ultra wide band (UWB) and cognitive radio systems.
The spectral efficiency of wireless communication system can be further improved by reusing frequencies in adjacent cells of the cellular system or in different wireless systems (e.g., cognitive radio) as needed. However, such reuse risks strong co-channel interference, which can dramatically degrade the performance of the system. Strong co-channel interference impact will become more and more important. Fortunately, with the development of antenna array/MIMO technology, using multiple antennas at the receiver to suppress strong co-channel interference is possible.
An MIMO-OFDM cellular system is shown in FIG. 1. The central cell 110, at a certain frequency and time, has one user 115 and three co-channel interference sources 125a, 125b and 125c from adjacent cells 120. When the user 115 and the co-channel interference sources 125 are at the edges of their cells, the signal to interference ratio (SIR) may be very low. To improve the system performance, the receivers can implement interference suppression schemes. The receivers can use interference rejection combining (IRC), which is used in 2G, 3G and possibly also in 3GPP LTE, to suppress the interference. See J. Ylitalo, E. Tiirola, “Performance Evaluation of Different Antenna Array Approaches for 3G CDMA Uplink”, Proceedings of 51st Annual International Vehicular Technology Conference (VTC'00), Tokyo, 2000, pp. 883-887.
It has been shown that M antennas can tolerate M−1 single interference streams. Therefore, using fewer antennas than interfering streams can degrade the system performance. See J. H. Winters, “Optimum Combining for Indoor Radio Systems with Multiple Users”, IEEE Transactions on Communications, 1987, Vol. 35, pp. 1222-1230.
In FIG. 2, a comparison of two interference conditions using an interference rejection combining (IRC) receiver is shown. In the first situation, there is one user and one interference source. Here, the user's signal power equals the interference power, i.e., SIRtotal=SIR1=0 dB. In the second situation, there are two interference sources. The user's signal power equals the total interference power and the power of the two interferences is the same, i.e., SIRtotal=0 dB; SIR1=SIR2. The channel is a Rayleigh one tap channel with a mobile speed of 90 km/h. The user and the interference sources each have one transmit antenna and the base station (BS) has two receive antennas.
Using a signal to Gaussian noise ratio: SNR=signal power/σ2, several conclusions can be drawn. When the SIR and noise power are low the bit error rate (BER) performance depends primarily on the interference. When the signal to total interference ratio is fixed, the two-antenna, IRC receiver is less effective for two interfering streams. If there is no interference, using IRC instead of MRC will not obtain a performance gain and will be more complex. Therefore, determining the number of interference sources can improve system performance.
IRC works well when there is only one strong interference signal. Using two antennas, the strong interference can effectively be suppressed when compared to maximum ratio combination (MRC). However, this does not cover all interference situations. There might be more than one strong interference source, which make IRC with two antennas inefficient.
Strong co-channel interference is often present in current wireless systems and greatly degrades the performance of the system. Thus, it is very useful to have the knowledge of any strong interfering streams. Some mechanisms could be employed at the transmitter or receiver according to the current interference conditions, which can make the wireless systems more robust and efficient.
An OFDM based ultra wide band (UWB) system could also benefit from identification of interfering streams. UWB utilizes an extremely wide frequency band with very low power to transmit signals. However, narrowband interference (NBI) will degrade the performance of UWB systems. Determining the presence of and location of interfering streams is significant to UWB systems. Thus, a flexible interfering stream identification scheme is needed.
In an OFDM based cognitive radio system, a licensed user and a secondary user from different wireless systems may co-exist in the same frequency band. If the licensed user doesn't use the channel at a given time, the secondary user from the other wireless system might use it to improve the frequency spectrum efficiency. However, since the licensed user has the priority of the channel, the secondary user should return the channel to the licensed user immediately when it is needed by the licensed user. As a result, identifying the licensed user's signal (which appears as interference to the secondary user) is needed in order to surrender the frequency band to the licensed user.
Based on the above description, it is very significant to come up with techniques to identify the interference. In Zilberfarb, “Interference source identification”, U.S. Pat. No. 5,048,015, a solution is presented that uses an identification code of the transmitters creating the interference. In Shah et al., “Method and system for identifying and analyzing downlink interference source in a telecommunication network”, U.S. Pat. No. 6,332,076 B1, a solution is presented that uses a database with time information of received interference signals. In Larsson et al., “Method for interference source identification”, WO 03/063532 and Chugg et al., “Method for co-channel interference identification and mitigation”, U.S. Pat. No. 7,010,069 B2, solutions are presented which require the use of ‘training sequences’ in the interference signals.
The above solutions use additional resources, such as an interference's training sequence, identification code, carrier information, etc. In some cases it may be impossible to get these resources. These prior solutions may also be very complex, e.g. the receivers may be required to test the training sequences of all the adjacent cells (6 adjacent cells in FIG. 1) to determine the number of interference sources even if there is only one source of interference.
As seen in E. Fishler and H. V. Poor, “Estimation of the number of sources in unbalanced arrays via information theoretic criteria,” IEEE Trans. of Signal Process., vol. 53, no. 9, pp. 3543-3553, September 2005, estimating the number of signal sources (which is considered in many fields such as bio-medical, microphone system, direction of arrival estimation and so on) through a sensor array has been a well investigated problem, which treats all the independent signals as desired signals instead of as interference. A common approach to solve this problem is to use an information theoretic criterion, such as minimum description length (MDL) or the Akaike Information Criterion (AIC). Such methods use many snapshots and computation and may be too complex to be implemented in practical mobile systems.